The paper develops a unified mathematical framework to analyze the interaction between post-quantum security, real-time communication constraints, and closed-loop stability in safety-critical turbofan…

The paper proposes a novel Meta-Quantum Ensemble (MQE) framework, which fuses outputs from Quantum Support Vector Machines (QSVMs) and Quantum Neural Networks (QNNs) using a Random Forest meta-learner…

This survey provides a detailed overview of quantum adversarial machine learning, examining existing attacks, novel quantum-enhanced defense strategies, and the theoretical challenges in securing quan…

The paper proposes Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network, to improve intrusion detection by modeling network flows as graphs and leveraging quantum circuits to capture complex rela…

The paper reviews adversarial machine learning vulnerabilities and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

The paper reviews the vulnerability of AI to adversarial attacks and proposes conceptual frameworks for enhancing AI robustness by integrating quantum computing techniques.

The paper proposes Q-FE, a novel Quantum-Native 6G Far-Edge architecture that secures Industrial IoT Digital Twins by integrating micro-digital twins, compact post-quantum key exchange, and asynchrono…

The paper presents Broken Quantum, a comprehensive formal security audit that identifies 547 security vulnerabilities across 45 open-source quantum computing simulators, revealing critical flaws in me…

This paper systematically analyzes the Quantum Bit Error Rate (QBER) in the BB84 Quantum Key Distribution protocol, demonstrating its use for quantifying channel noise and detecting eavesdropping, par…

The paper proposes a Quantum Augmented Microgrid (QuAM) framework that integrates quantum networking concepts to enhance the cybersecurity, confidentiality, and privacy of decentralized microgrids aga…

This study empirically benchmarks classical and quantum machine learning models for image recognition, finding that while quantum models offer superior accuracy and resource efficiency at high dimensi…

This paper develops and analyzes various ensemble models, culminating in an XGBoost-based system, to reliably detect UAV intrusions using XAI and advanced statistical methods to pinpoint the root caus…

This paper provides a comprehensive, system-level taxonomy for designing quantum-resistant network architectures, moving beyond simple protocol substitutions to address key distribution and management…

QML-PipeGuard introduces a contract-based framework that monitors the behavioral fingerprint of quantum machine learning pipelines to detect both hardware drift and malicious channel substitution.

The paper introduces a multi-surface evidence framework to provide comprehensive observability for post-quantum TLS migration, enabling robust measurement of session behavior and endpoint capabilities…

This paper demonstrates that neural operators used in digital twins for nuclear systems are highly vulnerable to undetectable, sparse adversarial perturbations, necessitating new robustness guarantees…

The paper proposes QShield, a hybrid quantum-classical neural network architecture, which significantly enhances the adversarial robustness of deep learning models against various attacks.

This paper introduces a quantum optimization framework using QAOA to perform Subgroup Discovery for network intrusion detection, demonstrating that quantum methods can find complex feature interaction…

The paper introduces Aquaman, a transparent-proxy architecture that enables quantum-resilient session-key establishment at the network edge, protecting clients that cannot natively support post-quantu…

This paper demonstrates that Unmanned Aerial Vehicle (UAV) autopilot fail-safe mechanisms are vulnerable to non-invasive voltage glitch fault injection, potentially allowing attackers to suppress crit…